The emergence of large language models (LLMs) like GPT-4 has revolutionized natural language processing (NLP), enabling diverse, complex tasks. However, extensive token counts lead to high computational and financial burdens. To address this, we propose Efficient and Flexible Prompt Compression (EFPC), a novel method unifying task-aware and task-agnostic compression for a favorable accuracy-efficiency trade-off. EFPC uses GPT-4 to generate compressed prompts and integrates them with original prompts for training. During training and inference, we selectively prepend user instructions and compress prompts based on predicted probabilities. EFPC is highly data-efficient, achieving significant performance with minimal data. Compared to the state-of-the-art method LLMLingua-2, EFPC achieves a 4.8% relative improvement in F1-score with 1% additional data at a 4x compression rate, and an 11.4% gain with 10% additional data on the LongBench single-doc QA benchmark. EFPC's unified framework supports broad applicability and enhances performance across various models, tasks, and domains, offering a practical advancement in NLP.

本研究解决了大型语言模型（LLM）在处理复杂自然语言任务时，由于标记数量庞大而造成的计算和经济负担问题。提出的高效灵活提示压缩（EFPC）方法，通过将任务感知与任务无关的压缩结合，达成了良好的准确性与效率的平衡。结果表明，EFPC在LongBench单文档问答基准测试中，表现较现有最优方法LLMLingua-2提升了4.8%，显示出其在各类模型、任务和领域中的广泛应用潜力。 

EFPC：朝着高效灵活的提示压缩迈进